# Self-play Learning Strategies for Resource Assignment in Open-RAN Networks

arXiv: https://arxiv.org/abs/2103.02649 · HTML: https://mlsanity.com/paper/2103.02649/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization](https://mlsanity.com/paper/1807.01672/) (2018)
- [Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges](https://mlsanity.com/paper/1907.08965/) (2019)
- [Learning Self-Game-Play Agents for Combinatorial Optimization Problems](https://mlsanity.com/paper/1903.03674/) (2019)
- [Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing](https://mlsanity.com/paper/1906.04594/) (2019)
- [Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning](https://mlsanity.com/paper/2006.07970/) (2020)
- [A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem](https://mlsanity.com/paper/1804.06896/) (2018)
- [Resource Allocation for Secure URLLC in Mission-Critical IoT Scenario](https://mlsanity.com/paper/1911.13154/) (2019)

## Built on by (most cited first, 8 of 8)

- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)
- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention](https://mlsanity.com/paper/2107.04333/) (2021)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym](https://mlsanity.com/paper/2208.14877/) (2022)
- [Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs](https://mlsanity.com/paper/2301.00597/) (2023)
- [Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning](https://mlsanity.com/paper/2401.08861/) (2024)
- [Optimizing 2D+1 Packing in Constrained Environments Using Deep Reinforcement Learning](https://mlsanity.com/paper/2503.17573/) (2025)

## Abstract (arXiv)

Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs), which allows flexible software on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the mapping of variable RU requirements to local mobile edge computing centres for future centralized processing would significantly reduce the power consumption in cellular networks. In this paper, we study the RU-DU resource assignment problem in an ORAN system, modelled as a 2D bin packing problem. A deep reinforcement learning-based self-play approach is proposed to achieve efficient RU-DU resource management, with AlphaGo Zero inspired neural Monte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing environment and real sites data show that the self-play learning strategy achieves intelligent RU-DU resource assignment for different network conditions.